Quantitative electroencephalographic analysis as a potential biomarker of response to treatment with cannabidiol

Cannabidiol and brain function: current knowledge and future perspectives

Cannabidiol (CBD) is a naturally occurring non-psychoactive cannabinoid found in Cannabis sativa, commonly known as cannabis or hemp. Although currently available CBD products do not meet the safety standards of most food safety authorities to be approved as a dietary supplement or food additive, CBD has been gaining widespread attention in recent years due to its various potential health benefits. While primarily known for its therapeutic effects in managing epileptic seizures, psychosis, anxiety, (neuropathic) pain, and inflammation, CBD's influence on brain function has also piqued the interest of researchers and individuals seeking to enhance cognitive performance. The primary objective of this review is to gather, synthesize, and consolidate scientifically proven evidence on the impact of CBD on brain function and its therapeutic significance in treating neurological and mental disorders. First, basic background information on CBD, including its biomolecular properties and mechanisms of action is presented. Next, evidence for CBD effects in the human brain is provided followed by a discussion on the potential implications of CBD as a neurotherapeutic agent. The potential effectiveness of CBD in reducing chronic pain is considered but also in reducing the symptoms of various brain disorders such as epilepsy, Alzheimer's, Huntington's and Parkinson's disease. Additionally, the implications of using CBD to manage psychiatric conditions such as psychosis, anxiety and fear, depression, and substance use disorders are explored. An overview of the beneficial effects of CBD on aspects of human behavior, such as sleep, motor control, cognition and memory, is then provided. As CBD products remain largely unregulated, it is crucial to address the ethical concerns associated with their use, including product quality, consistency, and safety. Therefore, this review discusses the need for responsible research and regulation of CBD to ensure its safety and efficacy as a therapeutic agent for brain disorders or to stimulate behavioral and cognitive abilities of healthy individuals.

Acute effect of antiseizure drugs on background oscillations in Scn1aA1783V Dravet syndrome mouse model

Dravet syndrome (Dravet) is a rare and severe form of developmental epileptic encephalopathy. Antiseizure medications (ASMs) for Dravet patients include valproic acid (VA) or clobazam (CLB), with or without stiripentol (STP), while sodium channel blockers like carbamazepine (CBZ) or lamotrigine (LTG) are contraindicated. In addition to their effect on epileptic phenotypes, ASMs were shown to modify the properties of background neuronal activity. Nevertheless, little is known about these background properties alterations in Dravet. Here, utilizing Dravet mice (DS, Scn1aA1783V/WT), we tested the acute effect of several ASMs on background electrocorticography (ECoG) activity and frequency of interictal spikes. Compared to wild-type mice, background ECoG activity in DS mice had lower power and reduced phase coherence, which was not corrected by any of the tested ASMs. However, acute administration of Dravet-recommended drugs, VA, CLB, or a combination of CLB + STP, caused, in most mice, a reduction in the frequency of interictal spikes, alongside an increase in the relative contribution of the beta frequency band. Conversely, CBZ and LTG increased the frequency of interictal spikes, with no effect on background spectral properties. Moreover, we uncovered a correlation between the reduction in interictal spike frequency, the drug-induced effect on the power of background activity, and a spectral shift toward higher frequency bands. Together, these data provide a comprehensive analysis of the effect of selected ASMs on the properties of background neuronal oscillations, and highlight a possible correlation between their effect on epilepsy and background activity.

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.